Tiny Machine Learning
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ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
arXiv:2606.02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized autoencoder-based TinyML models with minimal layers and parameters for embedded deployment.
The Last Evolution, by John W Campbell Jr. (1932)
The Project Gutenberg EBook of The Last Evolution, by John Wood Campbell This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org
AI-powered spectrometer chip shrinks lab technology to the size of a grain of sand
A new AI-powered chip from UC Davis can analyze light and chemicals using a device tiny enough to fit almost anywhere. By combining smart silicon sensors with machine learning, it achieves lab-style spectral analysis without the bulky equipment.
Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Summary: The article introduces Tiny-vLLM, a high-performance LLM inference engine developed in C++ and CUDA. The engine is designed to provide efficient and scalable inference for large language models, making it suitable for various applications such as natural language processing and machine learning. The article highlights the key features and benefits of Tiny-vLLM, including its ability to handle large models and its compatibility with various hardware platforms.
Learning-Augmented Online Minimization with Dual Predictions
arXiv:2606.05380v1 Announce Type: new Abstract: We present learning-augmented algorithms for two general classes of online minimization problems: metrical task systems and laminar set cover. Both algorithms achieve improved theoretical guarantees using machine-learned predictions of an optimal solution to the dual linear program.
Superintelligence: The Idea That Eats Smart People (2016)
This is the text version of a talk I gave on October 29, 2016, at Web Camp Zagreb [video] (45 mins) SuperintelligenceThe Idea That Eats Smart People | | | In 1945, as American physicists were preparing to test the atomic bomb, it occurred to someone to ask if such a test could set the atmosphere on fire. This was a legitimate concern.
The Smallest Brain You Can Build: A Perceptron in Python
A perceptron is the smallest brain you can build. One yes-or-no answer comes out. That is the whole thing.
AI paired with tiny optical device corrects distorted light for sharper imaging
AI paired with tiny optical device corrects distorted light for sharper imaging Gaby Clark Scientific Editor Robert Egan Associate Editor Blurry light from lens imperfections is a problem everywhere, from microscopes to telescopes to smartphone cameras. Using a tiny yet carefully engineered optical element and artificial intelligence, University of California San Diego engineers have built a way to spot and correct those distortions from a single image—a step that could make advanced optical...
Human-Like Neural Nets by Catapulting
Human-like Neural Nets by Catapulting Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence. There are many mysteries about deep learning and human intelligence, but we could describe the biggest anomaly this way: why are...
Researchers teach brain cells to play 'Doom'
Researchers teach brain cells to play 'Doom' Andrew Zinin Lead Editor Australian researchers have trained lab-grown brain cells on a silicon computer chip to play the nineties shooter game "Doom" and say they are just scratching the surface of what the neurons could be capable of doing. It's the science-fiction work of biotech boffins at Cortical Labs, who researched and developed the technology that harnesses the workings of the brain's networking system.